Native AI banks—built from the ground up around artificial intelligence—are automating customer service, credit, compliance, and more. Just look at how trailblazers like Catena Labs, One Zero, Bunq, WeBank, and CITIC aiBank are redefining global finance and challenging traditional banks.
AI in finance has evolved rapidly. In the 2010s, many banks introduced machine learning to credit scoring and chatbots to customer support, testing AI’s potential within existing frameworks. By 2020, leading banks were integrating advanced algorithms into risk management and customer personalization. A recent industry survey found that 65% of banks plan to launch AI-driven customer services in 2025 – evidence of how mainstream AI has become in banking. Yet, most of these efforts still graft AI onto legacy systems. In contrast, “AI-native” banks aim to design a financial institution entirely around AI capabilities, fundamentally reimagining how a bank operates .
The concept of native AI banks is gaining traction as entrepreneurs and technologists recognize that existing banks – even digital-first neobanks – face limitations in adapting to an AI-centric world. Traditional banks, built on decades-old processes and infrastructure, often find it “slow, expensive, full of global friction, inflexible and ill-suited” to support new opportunities that AI presents. This has opened the door for startups and forward-thinking financial firms to build banks that start with AI-first architectures.
These new players are designing systems where AI handles everything from customer interaction and fraud monitoring to credit decisions and even regulatory compliance, all under human oversight.
What Are Native AI Banks?
In simple terms, native AI banks are financial institutions built around artificial intelligence from day one, rather than bolting AI onto a traditional core.
A recent description by a fintech startup defined an AI-native bank as a bank “built around AI, not added as an afterthought.”
In practice, this means that the bank’s products, services, and internal processes are designed to be operated by AI algorithms and automation, with minimal manual intervention in day-to-day workflows. Human staff provide oversight, strategic guidance, and handle exceptional cases, but AI systems power the routine decisions and interactions.
A native AI bank typically features end-to-end digital operations with AI managing customer onboarding, risk assessment, transactions, and customer service.
Advanced machine learning models analyze customers’ data to offer personalized financial advice or detect fraud in real time. Chatbots and virtual assistants handle a large portion of customer inquiries. Importantly, these banks often incorporate the latest AI innovations such as generative AI for conversational interfaces or reinforcement learning for optimizing investment strategies. The goal is to create a bank that can learn and adapt continuously, improving its services as it gathers more data – something a static legacy core can’t easily do.
Another hallmark is that AI-native banks treat compliance and risk management as built-in features of the AI systems. In traditional banks, compliance is often a separate layer of checks and reports, some done manually. In an AI-first bank, the software is designed to respect regulatory constraints from the outset, automating things like suspicious activity monitoring. “A proper understanding of compliance and regulatory risk needs to have a seat at the table alongside product and engineering,” Neville emphasizes , indicating that these banks program regulatory logic directly into their AI workflows.
It’s important to note that “AI-native” doesn’t mean “AI-only.” Human oversight remains crucial.
The vision is not a completely autonomous bank with no employees, but a highly automated bank where humans and AI work in tandem. For example, one AI bank project plans to use “AI actors, or digital workers, as employees to perform internal tasks like writing software,” while humans handle oversight and high-level decision-making. In customer-facing roles, an AI assistant might answer routine questions, escalating to a human banker only when it encounters something it can’t handle or a situation requiring empathy and judgment.
In the following sections, we look at five initiatives that exemplify the native AI bank movement.
Catena Labs – Building a Bank for the “AI Economy”
One of the most talked-about new projects is Catena Labs, a U.S.-based startup co-founded by Sean Neville (best known as a co-founder of Circle, the company behind the USDC stablecoin).
Catena Labs grabbed headlines in May 2025 by securing $18 million in seed funding to build what Neville calls a “fully regulated, AI-native financial institution” designed for the emerging “AI economy.”
The funding round was led by Andreessen Horowitz’s a16z crypto fund, with participation from prominent investors including Breyer Capital, Coinbase Ventures, and even NFL star Tom Brady – a lineup that underscores the buzz around this idea.
Catena’s vision is ambitious: to create a bank where AI systems (referred to as “AI agents”) can hold accounts, execute transactions, and interact financially with other agents or humans autonomously. Neville believes that in the near future, “AI agents will soon conduct most economic transactions,” and that today’s banks are fundamentally unequipped for that scenario.
For instance, a trading algorithm or an e-commerce bot might need to make thousands of split-second payments or sign contracts on behalf of a human owner – tasks that strain conventional banking processes.
Catena’s answer is to rebuild financial infrastructure from scratch to accommodate such needs.
At the core of Catena’s approach is the use of stablecoins – specifically USDC, which Neville co-created – as “AI-native money” for transactions.
Because stablecoins run on blockchain networks, they enable near-instant, programmable payments across borders. Catena Labs argues that stablecoins are ideal for AI agents, which might operate 24/7 globally and require fast, low-cost transactions without human delays. By leveraging USDC and similar digital currencies, the new bank intends to let AI clients move money as seamlessly as data, while still adhering to regulatory standards for know-your-customer (KYC) and anti-money laundering (AML).
Regulation and trust are key focuses for Catena Labs.
Neville emphasizes that obtaining the proper banking licenses and ensuring compliance is integral to the project’s roadmap. The bank will be “operated by AI with human oversight,” meaning automated systems run day-to-day functions but humans set policies and intervene when needed. Catena has even released an Agent Commerce Kit (ACK) – an open-source toolkit for verifying and managing the identity of AI agents. Establishing trusted digital identity for AI entities is one of the thornier challenges, since regulations demand identification of account holders (and you obviously can’t fingerprint an AI bot). The ACK is an early attempt to solve this by providing protocols to register and authenticate AI agents in financial transactions .
In articulating why this effort is needed, Catena Labs does not mince words about the shortcomings of incumbent banking. The current global financial infrastructure is described as “slow, expensive, full of global friction, inflexible and ill-suited to the new opportunities and risks of AI.”
Traditional banks, in Neville’s view, actively block automated agents – for example, many systems are built to detect and prevent “bots” for security, which ironically becomes an obstacle when legitimate AI agents try to participate. Catena’s proposed bank, by contrast, would be built “so that AI actors will be the primary users, instead of blocking them.”
As of mid-2025, Catena Labs is still in development mode – the company has no public product yet and is working toward obtaining licenses. The $18 million infusion will accelerate hiring and product builds. Given Neville’s background at Circle, it’s likely the startup will work closely with regulators (possibly pursuing a banking charter or partnering with an existing bank) to ensure the AI-native bank launches on solid legal footing.
One Zero Bank – Israel’s AI-Driven Digital Bank
While some AI-native bank projects are just beginning, One Zero Bank in Israel is already operational and integrating AI deeply into its services.
Launched in late 2022, One Zero is Israel’s first fully digital bank – notably, the first new bank to receive a banking license in the country in over 45 years.
It was co-founded by Professor Amnon Shashua, a prominent technologist best known as the founder of Mobileye (a leader in self-driving car technology). Backed by substantial funding, One Zero Bank set out from the start to blend AI technology with banking. The bank described its model at launch as “driven by artificial intelligence, amalgamating the advantages of traditional and neo-banks.” In practice, One Zero combines digital convenience with a private-banking style experience, using AI to enhance customer service and personalization.
One Zero Bank has raised significant capital, underscoring the confidence in its approach. By 2025 the bank had raised around $242 million and was valued at about $320 million , with investors including tech giants like Tencent and fintech funds from SoftBank’s ecosystem.
AI is at the heart of One Zero’s customer experience.
In February 2024, the bank launched “Ella 2.0,” a generative AI-powered service platform that acts as a virtual financial assistant for customers. Developed in partnership with AI21 Labs (an Israeli AI startup specializing in large language models), Ella 2.0 is essentially an AI private banker available 24/7.
Customers can interact with Ella in natural language – asking complex questions about their finances across accounts, getting budgeting advice, or troubleshooting issues – and get instant, context-aware responses. The system understands multiple languages and was trained on extensive banking queries to improve its accuracy.
According to the bank, Ella 2.0 “delivers instantaneous responses, operates 24/7, and harnesses machine learning to tailor personalized financial services.” In other words, it continuously learns from customer interactions to offer better help, while human bankers stand by to support when needed.
The first CEO of One Zero, Gal Bar Dea, highlighted how this AI assistant elevates service quality. “Ella 2.0’s capabilities transcend linguistic barriers,” he said, ensuring “immediate, accurate, and personalised responses while continuously evolving to meet individual customer needs.”
One Zero takes pride in leading this “global charge from experimental Generative AI to practical implementation” in banking.
Ori Goshen, co-CEO of AI21 Labs, noted that “One Zero’s new AI assistant, Ella, represents a shift in the digital banking industry towards a better customer experience – one that is faster, more reliable, and personalised to each user.”
Such endorsements underscore how closely integrated the tech startup and the bank are in developing AI solutions.
Beyond Ella, One Zero uses AI in more behind-the-scenes ways. Automated algorithms handle much of the bank’s daily operations and decision-making. For example, AI models are employed for credit risk assessments and investment recommendations, learning from data to refine their outputs.
The bank’s strategy has been to automate routine tasks as much as possible, which reduces costs and allows the bank to offer more competitive fees.
At the same time, One Zero maintains human financial advisors that clients can reach out to (the bank promises a hybrid of “personal financial managers” and AI assistance). This dual approach caters to customers who want the efficiency of AI but also the reassurance of human expertise for important decisions.
One Zero’s heavy investment in AI is paying off in customer engagement.
By some reports, its AI assistant was handling up to 40% of customer inquiries independently shortly after launch, and assisting human agents with many others. This significantly cuts down response times – the bank claims to have eliminated wait times for most queries – and it ensures that customers get consistent, high-quality answers anytime.
The AI can even handle complex cross-referenced questions; One Zero noted scenarios like asking “What was that Indian restaurant I went to with a friend in London?” and the system can infer and find the transaction. Such capabilities illustrate the power of combining transaction data with conversational AI.
From a market perspective, One Zero Bank is a case study in how a new bank can differentiate via AI. In Israel’s competitive banking sector, One Zero’s selling point is not just that it has a slick mobile app – many banks do – but that its services are smarter and more proactive. The bank can alert users of unusual spending, forecast their cash flow, or suggest financial moves, driven by AI analytics on their data. This aligns with a broader trend: consumers increasingly expect personalized, instant service in finance, similar to how Netflix or Spotify personalize entertainment. One Zero is tapping into that expectation, using AI to become a “financial concierge” of sorts.
Challenges remain for One Zero, especially as it eyes expansion beyond Israel. The bank had plans to expand internationally, but external events (such as regional conflicts in late 2023) forced it to pause some initiatives.
Nonetheless, the company’s progress is being watched globally. If One Zero Bank continues to succeed, it could inspire similar AI-focused digital banks in other countries. It also provides a live example to regulators of how AI can be safely integrated into banking. Notably, Israel’s regulators gave One Zero a full banking license, indicating trust in its model and capital – a positive sign for other AI-native bank hopefuls seeking regulatory approval in the future.
Bunq – Europe’s First AI-Powered Neobank
In Europe, one of the established players embracing an AI-native approach is Bunq, a Dutch digital bank often dubbed “the bank of The Free” for its tech-driven, user-centric ethos.
Bunq was founded in 2012 and has grown to millions of users across Europe, but in late 2023 it made waves by announcing that it had become “Europe’s first AI-powered bank.”
Bunq integrated generative AI into its platform to a degree not seen among its peers, aiming to transform how customers interact with their finances. The centerpiece of this effort is “Finn,” Bunq’s AI-powered personal finance assistant.
In December 2023, Bunq rolled out Finn as a customer-facing generative AI tool embedded in its app.
Finn effectively replaced the traditional search and navigation functions within the Bunq app. Instead of manually browsing menus or transaction lists, users can simply ask Finn questions or give commands in natural language. “Finn will wow you,” Bunq’s founder and CEO Ali Niknam said at the launch, touting the result of “years of AI innovation” and a “laser focus on our users.”
The goal, as Niknam described, was to “completely transform banking as you know it” by making interactions as easy as a conversation .
What can Finn do? According to Bunq, a lot. Users can ask questions like, “How much did I spend on groceries last month?” or “What’s my average monthly utility bill?”, and Finn will instantly parse their transaction data to give an answer. It can also handle more complex queries that combine multiple pieces of information.
For example, Niknam shared that “it can even combine data to answer questions that go beyond transactions, such as ‘How much did I spend at the café near Central Park last Saturday?’”. The AI is context-aware, meaning it can figure out that “the café near Central Park” refers to a specific merchant and date in the user’s transaction history, something a normal search function would struggle with. By enabling such conversational queries, Bunq makes it far easier for users to analyze their own spending and find information without accounting knowledge or tedious manual effort.
Beyond Q&A, Finn assists with financial planning and budgeting. Users can ask for advice or insights, like “Do I have enough surplus this month to add €500 to my savings?” and get a data-driven response. It’s like having a personal accountant on call.
Bunq leverages this to encourage healthier financial habits among its customers. Internally, Bunq’s AI also analyzes transaction patterns across multiple linked accounts (using Europe’s open banking frameworks) to give consolidated views of a user’s finances. This means Finn can see a customer’s balances and spending not just at Bunq, but at other banks if the user permits, providing a one-stop overview – a powerful feature for budgeting and planning.
The impact of Finn was notable.
Reports indicated that Finn was able to handle about 40% of customer queries on its own, without human intervention, and assist with another significant portion.
This reduced the workload on Bunq’s support staff and accelerated response times for users. In fact, by early 2024 Bunq claimed that Finn’s introduction had made customer interactions more efficient than ever, with many questions answered instantly by the AI. For the remaining queries requiring a human touch, Bunq’s team could focus on complex issues, now that the AI triages the simple ones.
The result is a scalable customer service model as Bunq continues to grow its user base across Europe.
Bunq’s embrace of AI comes as it is expanding geographically and in products. The company applied for a U.S. banking license in 2023 , aiming to enter the American market, and such innovation helps it stand out in an increasingly crowded neobank scene.
It’s worth noting that other fintechs are following suit: U.S. neobank MoneyLion announced a ChatGPT-powered search feature around the same time , and another called Dave introduced “DaveGPT” for customer inquiries.
But Bunq’s head start and integration into core functionality (replacing search entirely with AI) gave it a leadership claim.
From a business perspective, Bunq uses AI not only to help users but also to derive insights that inform new offerings. By analyzing how people ask questions about their money, Bunq can identify pain points or popular requests and potentially create new features or products around those.
For example, if many users ask “Can I afford X by end of year?”, Bunq might develop an automated savings planner. This data-driven innovation is a competitive advantage of being an AI-native bank – the feedback loop from user interactions to service improvement is very tight.
However, Bunq is also careful to couple AI with human oversight. All AI responses are monitored for accuracy and relevance.
The bank has emphasized that Finn’s advice is based on data but customers should exercise judgment – it’s an assistant, not a fully autonomous financial manager (at least not yet). Additionally, privacy and security are paramount; Bunq has to ensure that the AI only accesses data the user has permissioned and that sensitive information is protected. So far, no major issues have been reported, and customers have largely responded positively to the convenience of conversational banking.
Ali Niknam, Bunq’s CEO, has framed the AI push as part of Bunq’s mission to simplify banking. In his view, traditional banks burden customers with clunky interfaces and jargon, whereas Bunq wants to “make life so much easier” for users through technology.
By making banking as easy as texting a friend, Bunq hopes to deepen customer loyalty and engagement. Indeed, industry analysis shows that personalization and ease of use significantly boost customer satisfaction in banking.
Bunq’s AI strategy hits both targets: personalize the experience (since Finn’s answers are unique to your data and questions) and make it easy (no need to learn the app menus or finance terminology).
As one of the first movers in AI-powered banking in Europe, Bunq offers a valuable example for the industry. It demonstrates that even an operational bank with millions of users can successfully infuse AI at the core of its services – it’s not just something for brand-new startups. Bunq’s experience will be closely watched by other European banks and fintechs. In a way, Bunq is turning into a tech company as much as a bank, continually integrating the latest AI developments. If Finn and subsequent AI features continue to perform well, it’s likely we’ll see more banks launching their own GPT-style assistants or AI-driven personalization features in an arms race to attract digitally savvy customers.
WeBank – China’s Pioneering AI-First Bank
No discussion of AI in banking would be complete without WeBank, China’s trailblazing digital bank that has been a pioneer in AI adoption since its inception.
WeBank was founded in 2014 as China’s first internet-only bank, backed by tech giant Tencent. From the beginning, WeBank’s strategy was to leverage cutting-edge technologies – encapsulated in its “ABCD” mantra (AI, Blockchain, Cloud, Data) – to serve millions of customers at low cost. Over the past decade, WeBank has grown explosively, providing loans, payments, and financial services to tens of millions of users, many of them underbanked individuals and small businesses. Its success is often credited to its deep integration of AI in operations, enabling it to manage volume and risk far more efficiently than traditional banks.
One of WeBank’s notable achievements is the extent to which it uses AI and automation in customer service and support. As of a few years ago, WeBank reported that it was receiving around 100,000 customer service queries per day, and its AI “virtual robots” were handling 98% of them without human intervention.
These virtual agents use natural language processing and speech recognition – essentially early versions of the kind of AI that powers today’s voice assistants – to resolve customer inquiries. Dr. Yang Qiang, a chief AI consultant at WeBank, explained that they deploy facial recognition, voice recognition, and NLP to improve service and convenience. Customers can interact through chat or voice, and the AI can authenticate them (via facial recognition) and address issues or execute requests in real time.
WeBank’s philosophy has been that AI is there to “augment, not replace” human service – a stance that sounds similar to Western banks, but WeBank has taken it to an extreme degree of implementation. “Automated service is not an enemy to human services. They should work side by side,” Yang Qiang told CNBC. The result is a highly scalable model: a relatively small team of human staff can oversee a customer base of millions because AI is doing the heavy lifting day-to-day. In fact, WeBank famously started with only a few dozen employees and no physical branches, yet it was able to disburse enormous volumes of micro-loans across China by relying on AI-driven credit algorithms and customer interactions through smartphones. This operational efficiency is a major reason WeBank turned profitable within just a couple of years of launch, a rare feat for a new bank.
Another area where WeBank shines is AI-driven credit risk analysis and loan approval.
Traditional banks often require lengthy paperwork and human underwriting for loans, but WeBank automated much of that using machine learning models. By analyzing vast amounts of alternative data – such as social media behavior, mobile payment history (leveraging Tencent’s ecosystem), and other digital footprints – WeBank’s AI can assess creditworthiness quickly and extend small loans to individuals and SMEs that might be rejected by larger banks.
This inclusive approach has extended credit to segments previously deemed too risky or costly to serve. Yang Qiang noted that such technology creates “the possibility for WeBank to have more efficiency than traditional banks in processing loans and conducting risk analysis”, which indeed has been borne out. WeBank can process loan applications in minutes and monitor them continuously, something legacy banks find hard to match.
WeBank has also been an innovator in AI research.
It has invested in areas like federated learning, a technique to train AI models on sensitive data from multiple sources without compromising privacy. This was important for WeBank to collaborate with other institutions (like sharing fraud data) while respecting China’s strict data privacy rules.
The bank’s technologists have published papers and open-sourced tools, indicating that WeBank sees itself as a tech leader, not just a financial services company. In March 2025, WeBank even shared a vision for an “AI-native bank” at a global conference, highlighting how a decade of its tech expertise is pushing banking to be “smarter and more inclusive.”
This suggests WeBank is aiming to stay at the forefront of AI in finance, possibly exploring next-gen AI like generative models for even more advanced services.
Despite its tremendous automation, WeBank hasn’t eliminated the human element. Instead, it has reallocated it. With AI doing routine work, human employees focus on areas like improving algorithms, handling exceptional cases, and developing new products.
WeBank’s staffing strategy reportedly has about 60% of employees in technology roles – an unusually high ratio for a bank, but logical for what is essentially a fintech institution. This tech-first culture further cements WeBank’s status as an AI-native bank avant la lettre.
CITIC aiBank – A Joint Venture of Finance and Tech
Around the same time WeBank was taking off, another notable experiment in AI-centric banking was underway in China: CITIC aiBank (often just called “AiBank”).
This is a joint venture between China Citic Bank, a mid-tier commercial bank, and Baidu, the internet search and AI giant. Launched in late 2017, aiBank was established as a direct, branchless bank with the explicit goal of leveraging big data and artificial intelligence to deliver smarter financial services.
With a registered capital of 2 billion yuan (about $300 million at the time) and ownership split 70/30 between Citic Bank and Baidu, aiBank represented a blend of banking domain knowledge and cutting-edge tech capability.
AiBank’s focus from the start was on lending to consumers and small businesses, segments often underserved by traditional banks in China. By using Baidu’s AI technology, aiBank aimed to develop new risk assessment models that could better evaluate borrowers who lack extensive credit histories. “AiBank will focus on lending to individuals and small businesses while leveraging big data and artificial intelligence to build new risk control models,” said Li Rudong, the bank’s president, at its launch.
This indicates that aiBank intended to analyze non-traditional data – possibly including search data, social data, etc., thanks to Baidu – to make credit decisions. The expectation was that AI-driven insights could identify creditworthy customers that legacy scoring methods might overlook, thus profitably expanding financial inclusion.
A striking detail revealed at launch was that 60% of aiBank’s employees would be tech staff. This was essentially unheard of in banking at that time and signaled how differently aiBank would operate compared to a typical bank where most staff are in branches or general operations. By concentrating on engineering and data science talent, aiBank put itself on a path to continuously develop and refine AI systems in-house. Baidu’s contribution was not just capital but also technology – including its AI platforms, cloud services, and perhaps even its vast user data (within privacy/legal limits). This partnership was part of a broader trend in China of tech companies and banks teaming up – similarly, Alibaba with MYbank, and Tencent with WeBank – to create hybrid entities that marry the strengths of each. In Baidu’s case, aiBank also offered a way to monetize its AI research in finance and showcase its AI leadership.
At the launch event, Baidu’s then Chief Operating Officer, Lu Qi, heralded the venture by saying, “AiBank is the future of intelligent finance… It is an institution that understands customers best and understands finance best.” This quote captures the aspiration that by fusing Baidu’s knowledge of users (from their online behavior) with Citic’s banking expertise, aiBank could outperform traditional banks in customer insight and service.
Being a direct bank (online-only) also meant aiBank could reach customers nationwide without physical presence, a significant advantage in China’s vast market.
In practice, over the next few years, aiBank rolled out digital lending products and AI-enhanced services. It offered personal loans via mobile apps, with quick approvals powered by machine learning credit models. For small businesses, it experimented with using AI to analyze e-commerce transactions and supply chain data to extend credit – much like Ant Group does.
AiBank also explored AI in customer service, including intelligent chatbots for basic inquiries. Given Baidu’s strengths in natural language processing (Chinese-language NLP in particular), aiBank likely benefited from advanced AI in voice assistants and text-based customer interaction. While detailed performance data of aiBank is not widely public, its continued operation and capital increases (Citic and Baidu reportedly doubled its capital by 2018 to support growth ) suggest it gained traction.
One unique angle for aiBank is the synergy with Baidu’s ecosystem. Baidu could integrate aiBank’s financial services into its popular apps. For instance, users of Baidu’s search or maps might be offered aiBank services contextually (imagine searching for “car loan” and seeing an aiBank offer). Moreover, Baidu’s AI research, such as in facial recognition and voice tech, found a real-world use in aiBank’s security and onboarding processes. As Yang Qiang from WeBank mentioned generally, technologies like facial recognition can allow seamless, remote account opening – aiBank likely employed similar methods given Baidu’s expertise. In a sense, aiBank served as a platform for Baidu to demonstrate the power of AI in a regulated industry, potentially strengthening Baidu’s position in the AI business market.
However, running an AI-native bank within a larger traditional bank (Citic) structure also had challenges.
Citic Bank’s involvement ensured regulatory compliance and provided banking infrastructure, but it may have also imposed a more cautious pace than a pure startup. Regulatory oversight by the China Banking and Insurance Regulatory Commission (CBIRC) meant aiBank’s AI innovations had to align with financial risk regulations. In 2021, an anecdote emerged that Chinese regulators fined Citic and Baidu for some formalities in the JV’s formation – a reminder that even tech-forward banks operate under strict rules. Nonetheless, China’s regulators have been generally supportive of AI and fintech in banking, as long as risks are controlled.
As of 2025, CITIC aiBank stands as an example of a successful integration of AI in a new banking venture.
It may not have the global name recognition of WeBank, but it underscores a collaborative model: a legacy bank and a tech giant co-creating an AI-native banking platform.
Closing Thoughts
The rise of native AI banks points to a future where finance is faster, more personalized, and even machine-driven.
These pioneering projects demonstrate that banks can be radically rethought with modern technology – potentially offering customers ultra-convenient services and opening the financial system to new participants (like AI agents or underserved populations). Going forward, we can expect to see traditional banks respond by accelerating their own AI adoption or partnering with AI-native initiatives. In some cases, incumbents might acquire successful AI banking startups to bolt on their capabilities. Regulators, too, are paying close attention. If AI-native banks show strong performance in risk management and compliance, regulators may update frameworks to facilitate wider use of AI in banking, perhaps even creating new license categories for AI-driven financial institutions.
However, the advent of AI-native banks also brings significant risks and challenges that need to be managed. One major concern is governance and oversight. When AI algorithms make credit decisions or detect fraud, ensuring they are unbiased and error-free is critical. Unchecked algorithms could inadvertently redline certain customer groups or approve risky loans – mistakes that could erode trust and invite regulatory penalties. Transparency is another challenge: these banks must make their AI’s actions explainable to regulators and customers.
For traditional financial institutions, the emergence of AI-native banks is a double-edged sword. On one hand, it pushes the envelope of innovation, potentially yielding new methods and technologies that incumbents can adopt. Established banks can learn from the efficiency of Catena’s AI workflows or the customer engagement success of Bunq’s Finn, and integrate similar ideas. On the other hand, these new entrants could become formidable competitors in certain segments.